Unsupervised clustering algorithms sometimes do not lead to meaningful interpretations of the structure in the data. We propose a new approach in which the concept of cluster density is introduced to assess the quality of an algorithmically generated partition and accordingly guide an amelioration process through split-and-merge operations.